AWS Monitoring and Observability: From CloudWatch to Correlated Insight
CloudWatch tells you a metric crossed a line. It does not tell you why it happened, what else broke at the same moment, or what it means for the customer trying to check out. That gap, between knowing something is wrong and understanding it, is the difference between monitoring and observability. Most teams are stuck on the wrong side of it.
This guide covers both: how to monitor AWS properly with the native tools, where those tools run out of road, and what correlated observability gives you that a wall of separate dashboards never will.
Monitoring and observability are not the same thing
The words get used interchangeably. They should not be.
Monitoring answers a question you already knew to ask. Is CPU above 80 percent? Is the queue backing up? Is the endpoint returning 500s? You define the check in advance, and the system tells you when the answer changes. Monitoring is necessary, and it is not enough, because production fails in ways you did not predict.
Observability answers questions you did not know to ask in advance. When something breaks at 2am in a way nobody anticipated, can you interrogate the system and work out what happened? That requires more than a set of pre-defined checks. It requires the underlying signals (metrics, traces, and logs) to be rich enough, and connected enough, that you can follow the failure wherever it leads.
The practical test: when an incident starts, does your team know what is wrong within minutes, or do they spend the first half hour jumping between consoles trying to assemble the picture? The second is monitoring. The first is observability.
What to actually monitor
Before tooling, decide what matters. Teams that monitor everything end up paying attention to nothing, because the signal drowns in noise.
Start with the signals that map to customer experience. For request-driven services, the useful frame is latency, traffic, errors, and saturation: how fast are requests served, how many are there, how many fail, and how close to capacity are you. For resources, the frame is utilisation, saturation, and errors. These are deliberately small lists. They focus attention on the handful of signals that actually predict whether your service is healthy.
Then add the AWS-specific metrics that matter for the services you run. For EC2, that is CPU, network, and disk. For RDS, it is connections, read and write latency, replica lag, and freeable memory. For Lambda, it is duration, concurrency, throttles, and errors. For a load balancer, it is request count, target response time, and the count of 4xx and 5xx responses. Setting the right key metrics per service is the difference between an alert that means something and an alert everyone learns to ignore.
The goal is not maximum coverage. It is the smallest set of signals that reliably tells you when something is wrong and points at where.
CloudWatch: the foundation and its limits
CloudWatch is the native AWS monitoring service, and it is where most teams correctly begin. It collects metrics from AWS services automatically, accepts custom metrics from your applications, stores logs, and triggers alarms.
Metrics and alarms
CloudWatch alarms watch a metric and fire when it crosses a threshold for a defined period. Done well, they are the backbone of your alerting. Done badly, they are noise.
The best practices that matter: alarm on symptoms your users feel, not just on causes. High CPU is a cause; slow response time is a symptom. Alarm on the symptom and use the cause to diagnose. Use appropriate evaluation periods so a brief spike does not page someone at 3am, but a sustained problem does. Set thresholds from observed normal behaviour, not from round numbers that feel right. And treat composite alarms as a way to reduce noise: alarm when several conditions are true together, rather than firing five separate pages for one underlying problem.
Manually clicking together alarms across a growing estate does not scale. Define them as code and deploy them with your infrastructure so every new service arrives already monitored. We cover the mechanics in automate alerts with AWS CloudWatch.
Where CloudWatch runs out of road
CloudWatch is a solid foundation. It also has limits that become painful as your environment grows.
It is siloed by design. Metrics, logs, and traces live in separate places with separate query models, and stitching them together during an incident is manual work. Cross-service correlation is hard: when a slow API call is actually caused by a downstream database under load, CloudWatch will show you both facts but will not connect them for you. Multi-account and multi-region visibility requires deliberate aggregation work. And distributed tracing through X-Ray is a separate tool again, with its own console and its own learning curve.
None of this means CloudWatch is wrong. It means CloudWatch alone leaves your team doing the correlation by hand, under pressure, during an incident. That manual correlation is exactly where time goes.
The real problem is correlation
Picture a typical incident. Error rates climb. An engineer opens the application dashboard, sees the errors, then opens the infrastructure console to check the hosts, then opens the database metrics, then searches the logs in a fourth place, trying to line up timestamps across four tools to work out what happened first. Twenty minutes later they have a theory.
The failure was never a lack of data. The data was all there. The failure was that nothing connected it. Every minute spent assembling the picture by hand is a minute the customer is still affected.
This is the case for unified observability: metrics, traces, logs, and security signals in one platform, correlated automatically, so the question "what changed and what did it affect" has an answer in one place instead of four.
Unified observability, and why it changes incident response
When the signals are correlated, the work changes. A spike in error rate is automatically tied to the trace that shows the slow span, which is tied to the log lines from that exact request, which is tied to the deploy that went out twenty minutes earlier and the host it landed on. The investigation that used to take twenty minutes of console-hopping becomes a short path through connected data.
This is what Datadog does, and it is why we built Critical Cloud on it. In our own operations, correlating metrics, traces, logs, and security data in one platform delivers a 60 percent reduction in mean time to resolve incidents. That number is not a marketing figure. It is the difference between manual correlation across silos and a single connected view, measured across the environments we run.
The same correlation that speeds up incidents speeds up everything else: performance troubleshooting, capacity planning, and cost investigation. When you can see that a service's spend rose the same week its traffic pattern changed and a particular deploy shipped, the cause is obvious instead of theoretical.
Performance troubleshooting in practice
Most performance problems are not mysteries once you can see the whole path. A slow endpoint is slow somewhere specific: in application code, in a database query, in a downstream call, in a cold start, or in saturation of an underlying resource. The reason troubleshooting feels hard is that, with siloed tooling, you cannot see the whole path at once, so you guess and check.
Observability turns guessing into following. You start at the symptom the user feels (the slow request), follow the trace to the span that is actually slow, and from that span reach the logs and the resource metrics that explain it. The skill shifts from "knowing where to look" to "reading the path the data lays out for you." That is a far more teachable, far more repeatable skill, which matters when you are running on-call across a small team.
Build it right
Good observability is designed, not bolted on. A few principles hold across every environment we run.
Instrument at the application level, not just the infrastructure level. Host metrics tell you a box is busy. Application traces tell you which request, for which customer, doing which operation, is slow. The second is what you actually need at 2am.
Define what healthy looks like before you need to. Service level objectives turn vague worry into a clear line: this service should serve 99.9 percent of requests under 300 milliseconds. Once that line exists, alerting becomes obvious and arguments about whether something is "bad enough to page" disappear.
Keep the signal high. Every alert that fires and turns out to be nothing trains your team to ignore the next one. Ruthlessly tune out the noise so that when something pages, people move.
In regulated markets, observability is now a requirement
For businesses under financial services or cybersecurity regulation, observability has moved from operational nicety to regulatory expectation. DORA and NIS2 both require firms to detect and report significant incidents within defined timelines, and you cannot report what you did not detect in time. The FCA operational resilience rules require firms to stay within impact tolerances for their important business services, which is a direct function of how fast you detect and resolve. A 60 percent reduction in mean time to resolve is not just an operational win in that context, it is the difference between staying inside an impact tolerance and breaching it, and between reporting an incident within the regulatory window and missing it.
The audit trail matters as much as the speed. When a regulator asks what happened, when, and what you did about it, a correlated observability platform is where that evidence lives: the timeline of the incident, the signals that triggered it, the response, and the resolution, all in one place rather than reconstructed from four tools after the fact. Observability is how regulated firms evidence operational resilience, not just achieve it.
Where Critical Cloud comes in
Standing up Datadog properly, instrumenting your applications, defining the right alerts and objectives, and then operating the whole thing around the clock is a real job. It is the job we do.
Critical Cloud is the world's first Powered by Datadog accredited partner. We implement and operate full-stack observability on AWS as a managed service: infrastructure monitoring, application performance monitoring, log management, and security monitoring, correlated in one platform and watched 24/7 by our SRE team. The 60 percent MTTR reduction is what that correlation buys you, and we bring it as standard.
If your team is still assembling the picture by hand during incidents, see how managed Datadog works with Critical Cloud.